相关论文: Morphological classification of compact and extend…

Aims. This work investigates the potential of using the wavelength-dependence of galaxy structural parameters (S\'ersic index, n, and effective radius, Re) to separate galaxies into distinct types. Methods. A sample of nearby galaxies with…

Modulation classification, recognized as the intermediate step between signal detection and demodulation, is widely deployed in several modern wireless communication systems. Although many approaches have been studied in the last decades…

A new musical instrument classification method using convolutional neural networks (CNNs) is presented in this paper. Unlike the traditional methods, we investigated a scheme for classifying musical instruments using the learned features…

Morphological classification is a key piece of information to define samples of galaxies aiming to study the large-scale structure of the universe. In essence, the challenge is to build up a robust methodology to perform a reliable…

The radio astronomy community is rapidly adopting deep learning techniques to deal with the huge data volumes expected from the next generation of radio observatories. Bayesian neural networks (BNNs) provide a principled way to model…

In recent years the amount of publicly available astronomical data has increased exponentially, with a remarkable example being large scale multiepoch photometric surveys. This wealth of data poses challenges to the classical methodologies…

Convolutional neural networks (CNNs) have been successfully applied on both discriminative and generative modeling for music-related tasks. For a particular task, the trained CNN contains information representing the decision making or the…

In this work we explore the possibility of applying machine learning methods designed for one-dimensional problems to the task of galaxy image classification. The algorithms used for image classification typically rely on multiple costly…

X-shaped radio galaxies are a subclass of radio sources that exhibit a pair of secondary low surface brightness radio lobes oriented at an angle to the primary high surface brightness lobes. Sometimes, the secondary low brightened lobes…

Most methods for time series classification that attain state-of-the-art accuracy have high computational complexity, requiring significant training time even for smaller datasets, and are intractable for larger datasets. Additionally, many…

Deep learning has recently been successfully applied in automatic modulation classification by extracting and classifying radio features in an end-to-end way. However, deep learning-based radio modulation classifiers are lack of…

Graph-based neural network models are gaining traction in the field of representation learning due to their ability to uncover latent topological relationships between entities that are otherwise challenging to identify. These models have…

The classification of radio galaxies is central to understanding galaxy evolution, active galactic nuclei dynamics, and the large-scale structure of the universe. However, traditional manual techniques are inadequate for processing the…

We seek to develop an essentially analytical model for the evolution of Fanaroff-Riley Class II radio galaxies as they age individually and as their numbers vary with cosmological epoch. Such modeling is required in order to probe in more…

This paper investigates the processing of Frequency Modulated-Continuos Wave (FM-CW) radar signals for vehicle classification. In the last years deep learning has gained interest in several scientific fields and signal processing is not one…

We investigate the validity of the quasar - radio galaxy unification scenario and detect dust tori within radio galaxies of various types. Using VISIR on the VLT, we acquired sub-arcsecond (~0.40") resolution N-band images, at a wavelength…

The identification of $\gamma$-rays from the predominant hadronic-background is a key aspect in their ground-based detection using Imaging Atmospheric Cherenkov Telescopes (IACTs). While current methods are limited in their ability to…

Radio loud active galactic nuclei (RLAGNs) are often morphologically complex objects that can consist of multiple, spatially separated, components. Astronomers often rely on visual inspection to resolve radio component association. However,…

Multifiber optical spectroscopy has been performed on galaxies in the vicinity of strong, nearby radio galaxies. These radio galaxies were selected from the 3CR and B2 catalogs based on their exclusion from the Abell catalog, which is…

Deep neural networks (DNNs) designed for computer vision and natural language processing tasks cannot be directly applied to the radio frequency (RF) datasets. To address this challenge, we propose to convert the raw RF data to data types…